Ji-hye Kim, Ok-ran Jeong, Journal of Internet Computing and Services, Vol. 21, No. 1, pp. 79-85, Feb. 2020
10.7472/jksii.2020.21.1.79, Full Text:  HTML
Keywords: Spam Filtering, spam detection, New Words Detection, Knowledge Graph, ConceptNet

Abstract

Today, to block spam texts on smartphone, a simple string comparison between text messages and spam keywords or a blocking spam phone numbers is used. As results, spam text is sent in a gradually hanged way to prevent if from being automatically blocked. In particular, for words included in spam keywords, spam texts are sent to abnormal words using special characters, Chinese characters, and whitespace to prevent them from being detected by simple string match. There is a limit that traditional spam filtering methods can’t block these spam texts well. Therefore, new technologies are needed to respond to changing spam text messages. In this paper, we propose a knowledge graph-based new words detection mechanism that can detect new words frequently used in spam texts and respond to changing spam texts. Also, we show experimental results of the performance when detected Korean new words are applied to the Naive Bayes algorithm.

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